Faculty of Science Course Syllabus Department of Mathematics and Statistics Statistical Methods for Data Analysis and Inference STAT2080/MATH2080/ECON2280 Winter 2017 Instructor(s): Dr. Joanna Mills Flemming Joanna.Flemming@Dal.Ca Chase 103 Dr. Christophe Herbinger Christophe.Herbinger@Dal.Ca Biology 4056 Lectures: Laboratories: NA MWF 9:35am-10:25am HENRY HICKS ACADEMIC 212, 2:35pm-3:25pm LSC C242 Tutorials: TH 5:05pm-5:55pm, MCCAIN Aud-1 Course Description This is the usual sequel to STAT 1060.03 or STAT 2060.03. This course introduces a number of techniques for data analysis and inference commonly used in the experimental sciences. Topics covered include model building in linear models, multiple regression, analysis of variance, factorial designs, analysis of covariance using the general techniques for linear models and two and three way tables along with logistic regression. A natural sequel for this course is STAT 3340.03. Course Prerequisites STAT 1060.03 or STAT 2060.03 or DISP The material you are expected to be familiar with is the following. The computation and use of various measures of central tendency and variability; the preparation and interpretation of graphical displays of data such as boxplots, histograms and scatterplots; the normal and t distributions and the use of tables for these distributions; the difference between populations and samples, parameters and estimates; the concept of sampling distributions and why they are important; the construction and interpretation of confidence intervals; the elements of hypothesis testing; the formation of null and alternative hypotheses and the computation and interpretation of p-values. Course Objectives/Learning Outcomes The main objective of this course is to provide a solid grounding in practical data analysis and common statistical methods that one encounters in scientific research. Towards this end the central emphasis of the course is on Analysis of Variance (ANOVA) and Regression. Outcomes: Full understanding of the statistical comparison of two means using both parametric and nonparametric methods, Full understanding of one-way and two-way analysis of variance (including assumptions, setup, calculations of key quantities, interpretation, and post-hoc diagnostics),
Full understanding of correlation as a measure of dependence, including both parametric (Pearson s) and non-parametric (Spearman s) measures of correlation, Full understanding of regression methods for simple linear regression(assumptions, key quantities and formulae, implementation, interpretation, and graphical assessment via residuals) Basic understanding of multiple regression (assumptions, key quantities and formulae, implementation, interpretation, and graphical assessment via residuals), Experience in the statistical analysis of categorical/count data in one-way and two-way tables (e.g. chi-squared tests and contingency tables), Ability to use modern statistical software (MINITAB). Course Materials There is an BrightSpace site for the course. This is where class notes, assignment information and announcements will be posted. Students are encouraged to use the discussion board for questions about assignments etc. There is no required text for this course. However, a detailed set of course notes will be provided. Readings will be suggested from the books used recently in STAT 1060 (Stats, Data and Models by DeVeaux, Velleman and Bock), and STAT 2060 (Probability and Statistics by J. Devore). The Minitab statistical package will be used in the course. It will be required for portions of some assignments, and sometimes used for demonstration in the lectures. The LON-CAPA (Learning Online Network with Computer-Assisted Personalized Approach) e-learning software will be used for assignments, and for the midterms (as well as for disseminating assignment and midterm marks). LON-CAPA can be accessed from the BrightSpace course space, or directly at capa.mathstat.dal.ca. Details on its use will be provided at the beginning of the course. The Mathematics and Statistics Student Resource Centre is in Room 119 of the Chase building. Please refer to the website {http://www.dal.ca/faculty/science/math-stats/about/learning-centre.html} where you can find a link to a schedule and when tutors with expertise in Statistics will be there and available to answer questions (on a first come first served basis). There are large tables available for groups to work together. Tutors from the Resource Center will also be available in the Learning Commons at the Killam library. Course Assessment Component Weight (% of final grade) Date Tests/quizzes: Midterm 1 (15%) February 16th Midterm 2 (15%) March 30th Final exam: (45%) (Scheduled by Registrar) Assignments: Weekly Assignments (25%) Weekly Other course requirements : N/A
Conversion of numerical grades to Final Letter Grades follows the Dalhousie Common Grade Scale A+ (90-100) B+ (77-79) C+ (65-69) D (50-54) A (85-89) B (73-76) C (60-64) F (<50) A- (80-84) B- (70-72) C- (55-59) Course Policies There will be nine assignments. These will be online assignments delivered using the CAPA software (see http://capa.mathstat.dal.ca). Late assignments are not accepted. If there is a legitimate conflict with the times of the midterm exams (this means another course or an exam scheduled for the same time), students must inform a professor of this at least 3 weeks in advance of the exam with details of the conflict. If an exam is missed for medical reasons, students must contact a professor within 24 hours of the exam and provide a medical excuse within 48 hours. If an exam is missed without a valid reason a zero grade may be assigned. The morning and afternoon sections are following the same schedule. If one section is cancelled on a particular date, the other section will be cancelled as well. Cell phones and other texting devices should be turned OFF before class begins. Course Content Listed below in roughly chronological order are the topics to be covered. Note that these may be altered slightly as the term progresses. Study design, causal inference and inference to population The central limit theorem; hypothesis testing and confidence intervals Comparison of two means - paired samples and independent samples Comparison of two means - permutation test, Wilcoxon rank-sum test One-way analysis of variance Bonferroni method for multiple comparisons Assessing the model assumptions - residual plot Non-parametric one-way ANOVA - Kruskall-Wallis test Two-way ANOVA without interaction Two-way ANOVA, with interaction, Randomized block design, Post-hoc comparisons of means Categorical data, multinomial distribution and goodness of fit test Chi-square tests and contingency tables Scatterplots, Pearson's correlation, Spearman's rank correlation Regression and least squares estimates Coefficient of determination, Residual plots, remedies and transformation Inference in regression Multiple regression basics, hypothesis testing and inference Issues in multiple regression ANOVA using regression Special topics and review
ACCOMMODATION POLICY FOR STUDENTS Students may request accommodation as a result of barriers related to disability, religious obligation, or any characteristic protected under Canadian Human Rights legislation. The full text of Dalhousie s Student Accommodation Policy can be accessed here: http://www.dal.ca/dept/university_secretariat/policies/academic/student-accommodation-policy-wefsep--1--2014.html Students who require accommodation for classroom participation or the writing of tests and exams should make their request to the Advising and Access Services Centre (AASC)prior to or at the outset of the regular academic year. More information and the Request for Accommodation form are available at www.dal.ca/access. ACADEMIC INTEGRITY Academic integrity, with its embodied values, is seen as a foundation of Dalhousie University. It is the responsibility of all students to be familiar with behaviours and practices associated with academic integrity. Instructors are required to forward any suspected cases of plagiarism or other forms of academic cheating to the Academic Integrity Officer for their Faculty. The Academic Integrity website (http://academicintegrity.dal.ca) provides students and faculty with information on plagiarism and other forms of academic dishonesty, and has resources to help students succeed honestly. The full text of Dalhousie s Policy on Intellectual Honesty and Faculty Discipline Proceduresis available here: http://www.dal.ca/dept/university_secretariat/academic-integrity/academic-policies.html STUDENT CODE OF CONDUCT Dalhousie University has a student code of conduct, and it is expected that students will adhere to the code during their participation in lectures and other activities associated with this course. In general: The University treats students as adults free to organize their own personal lives, behaviour and associations subject only to the law, and to University regulations that are necessary to protect the integrity and proper functioning of the academic and non academic programs and activities of the University or its faculties, schools or departments; the peaceful and safe enjoyment of University facilities by other members of the University and the public; the freedom of members of the University to participate reasonably in the programs of the University and in activities on the University's premises; the property of the University or its members. The full text of the code can be found here: http://www.dal.ca/dept/university_secretariat/policies/student-life/code-of-student-conduct.html
COPYRIGHT All members of the Dalhousie community are expected to comply with their obligations under Canadian copyright law. Dalhousie copyright policies and guidelines, including our Fair Dealing Guidelines, are available athttp://www.dal.ca/dept/copyrightoffice.html. Copyright questions should be directed to the Copyright Office at copyright.office@dal.ca. SERVICES AVAILABLE TO STUDENTS The following campus services are available to help students develop skills in library research, scientific writing, and effective study habits. The services are available to all Dalhousie students and, unless noted otherwise, are free. Service Support Provided Location Contact General Academic Advising In person: Killam Library Rm G28 Dalhousie Libraries Studying for Success (SFS) Help with - understanding degree requirements and academic regulations - choosing your major - achieving your educational or career goals - dealing with academic or other difficulties Help to find books and articles for assignments Help with citing sources in the text of your paper and preparation of bibliography Help to develop essential study skills through small group workshops or oneon-one coaching sessions Match to a tutor for help in course-specific content (for a reasonable fee) Killam LibraryGround floor Rm G28 Bissett Centre for Academic Success Killam Library Ground floor Librarian offices Killam Library3 rd floor Coordinator Rm 3104 Study Coaches Rm 3103 By appointment: - e-mail: advising@dal.ca - Phone: (902) 494-3077 - Book online through MyDal In person: Service Point (Ground floor) By appointment: Identify your subject librarian (URL below) and contact by email or phone to arrange a time: http://dal.beta.libguides.com/sb.php?subject_id=34328 To make an appointment: - Visit main office (Killam Library main floor, Rm G28) - Call (902) 494-3077 - email Coordinator at: sfs@dal.ca or - Simply drop in to see us during posted office hours All information can be found on our website: www.dal.ca/sfs